New theoretical frameworks for machine learning
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Cites methods from "New theoretical frameworks for mach..."
...Most instruction control flow analysis and optimization techniques can be transformed into probabilistic predictors via a technique from machine learning called “the kernel trick” [6, 17]....
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References
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"New theoretical frameworks for mach..." refers background in this paper
...Typical kernel functions for structured data include the polynomial kernel K(x, x′) = (1 + x · x′)d and the Gaussian kernel K(x, x′) = e−||x−x||(2)/2σ(2) , and a number of special-purpose kernels have been developed for sequence data, image data, and other types of data as well [55, 56, 93, 103, 118]....
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28,685 citations
"New theoretical frameworks for mach..." refers background in this paper
...121 5.2.2 The Non-realizable Case under the Uniform Distribution . . . . . . . . . . . . . . 125 5.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 x 5.3 Other Results in Active Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
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...) At a high level, in [35, 42] we point out that traditional anal yses [96] have studied the number of label requests required before an algorithm can both produce an ǫ-good classifier and prove that the classifier’s error is no more than ǫ....
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...Another learning setting where unlabeled data is useful and which has been increasingly popular for the past few years is Active Learning[31, 34, 35, 42, 87, 96]....
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...• In Chapter 5 we analyze Active Learning and present two main results....
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...(Full details of the model and results can be found in [35, 42] ....
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26,531 citations
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...To obtain our bounds we use and extend sample-complexity techniques from machine learning theory (see [11, 42, 88, 121]) and to design our mechanisms we employ machine learning methods such as structural risk minimization....
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17,939 citations
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...Note that any given tree has at most 22k prunings of size k [154], so this model is at least as strict as the list model....
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